电网技术2017，Vol.41Issue(8)：2593-2597,5.DOI:10.13335/j.1000-3673.pst.2016.0004
基于随机森林理论的配电变压器重过载预测
Heavy Overload Forecasting of Distribution Transformers Based on Random Forest Theory
摘要
Abstract
In view of problem that prediction of heavily overloaded distribution transformers with traditional classifier brings higher overall correct rate and lower heavy overload correct rate due to low sampling rate of heavy overload, a resampling and random forest regression method is introduced to classification model to predict heavily overloaded distribution transformers with resampling and random forest regression classification model. Firstly, heavy overload samples and normal samples from observation were randomly sampled to form a new sub sample according to a certain ratio, thus large number of new sub samples were obtained by repeating above process. Then, according to random forest theory, a series of classifiers are constructed, and the classification model is trained with sub samples. Final prediction is decided by all classifiers' prediction results. In comparison of performances between above method and traditional classifiers in prediction of heavily overloaded distribution transformers in Shandong Province, results show that the new method has higher accuracy in predicting heavy overload types, starting and ending times and heavy overload severity.关键词
配变重过载/样本比率/分类器预测/重抽样/随机森林Key words
heavy overload distribution transformers/sample ratio/classification prediction/resampling/random forest分类
信息技术与安全科学引用本文复制引用
贺建章,王海波,季知祥,孟祥君,张涛..基于随机森林理论的配电变压器重过载预测[J].电网技术,2017,41(8):2593-2597,5.基金项目
国家电网公司科技项目资助(XX71-14-036).Project Supported by State Grid Corporation of China Research Program (XX71-14-036). （XX71-14-036）
